






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Any:
  UpperCamelCase_:   List[str]        =							('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

  UpperCamelCase_:   Optional[int]        =							(
      ('layer.', 'layer_'),
      ('word_embeddings.weight', 'word_embeddings'),
      ('position_embeddings.weight', 'position_embeddings'),
      ('token_type_embeddings.weight', 'token_type_embeddings'),
      ('.', '/'),
      ('LayerNorm/weight', 'LayerNorm/gamma'),
      ('LayerNorm/bias', 'LayerNorm/beta'),
      ('weight', 'kernel'),
  )

  if not os.path.isdir(UpperCAmelCase__    ):
    os.makedirs(UpperCAmelCase__    )

  UpperCamelCase_:   Tuple        =							model.state_dict()

  def to_tf_var_name(UpperCAmelCase__    ):
    for patt, repl in iter(UpperCAmelCase__    ):
      UpperCamelCase_:   Tuple        =							name.replace(UpperCAmelCase__      ,       UpperCAmelCase__    )
    return F'''bert/{name}'''

  def create_tf_var(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    ):
    UpperCamelCase_:   int        =							tf.dtypes.as_dtype(tensor.dtype    )
    UpperCamelCase_:   Optional[int]        =							tf.get_variable(dtype=UpperCAmelCase__      ,       shape=tensor.shape      ,       name=UpperCAmelCase__      ,       initializer=tf.zeros_initializer()    )
    session.run(tf.variables_initializer([tf_var]    )    )
    session.run(UpperCAmelCase__    )
    return tf_var

  tf.reset_default_graph()
  with tf.Session() as session:
    for var_name in state_dict:
      UpperCamelCase_:   Tuple        =							to_tf_var_name(UpperCAmelCase__    )
      UpperCamelCase_:   str        =							state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose    ):
        UpperCamelCase_:   str        =							torch_tensor.T
      UpperCamelCase_:   Optional[int]        =							create_tf_var(tensor=UpperCAmelCase__      ,       name=UpperCAmelCase__      ,       session=UpperCAmelCase__    )
      tf.keras.backend.set_value(UpperCAmelCase__      ,       UpperCAmelCase__    )
      UpperCamelCase_:   List[str]        =							session.run(UpperCAmelCase__    )
      print(F'''Successfully created {tf_name}: {np.allclose(UpperCAmelCase__      ,       UpperCAmelCase__    )}'''    )

    UpperCamelCase_:   Tuple        =							tf.train.Saver(tf.trainable_variables()    )
    saver.save(UpperCAmelCase__      ,       os.path.join(UpperCAmelCase__      ,       model_name.replace('-'      ,       '_'    ) + '.ckpt'    )    )

def     snake_case   (UpperCAmelCase__=None    )			->							List[Any]:
  UpperCamelCase_:   int        =							argparse.ArgumentParser()
  parser.add_argument('--model_name'      ,       type=UpperCAmelCase__      ,       required=UpperCAmelCase__      ,       help='model name e.g. bert-base-uncased'    )
  parser.add_argument(
      '--cache_dir'      ,       type=UpperCAmelCase__      ,       default=UpperCAmelCase__      ,       required=UpperCAmelCase__      ,       help='Directory containing pytorch model'    )
  parser.add_argument('--pytorch_model_path'      ,       type=UpperCAmelCase__      ,       required=UpperCAmelCase__      ,       help='/path/to/<pytorch-model-name>.bin'    )
  parser.add_argument('--tf_cache_dir'      ,       type=UpperCAmelCase__      ,       required=UpperCAmelCase__      ,       help='Directory in which to save tensorflow model'    )
  UpperCamelCase_:   Union[str, Any]        =							parser.parse_args(UpperCAmelCase__    )

  UpperCamelCase_:   int        =							BertModel.from_pretrained(
      pretrained_model_name_or_path=args.model_name      ,       state_dict=torch.load(args.pytorch_model_path    )      ,       cache_dir=args.cache_dir      ,       )

  convert_pytorch_checkpoint_to_tf(model=UpperCAmelCase__      ,       ckpt_dir=args.tf_cache_dir      ,       model_name=args.model_name    )


if __name__ == "__main__":
   main()

"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 			lowercase					( lowercase__		):
			lowercase        =     ['''image_processor''', '''tokenizer''']
			lowercase        =     '''BlipImageProcessor'''
			lowercase        =     ('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(self  :	Any      ,SCREAMING_SNAKE_CASE_  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  False
				super().__init__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  self.image_processor






			def __call__(self  :	int      ,SCREAMING_SNAKE_CASE_  :	ImageInput = None      ,SCREAMING_SNAKE_CASE_  :	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Union[bool, str, PaddingStrategy] = False      ,SCREAMING_SNAKE_CASE_  :	Union[bool, str, TruncationStrategy] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	int = 0      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[bool] = None      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = False      ,SCREAMING_SNAKE_CASE_  :	bool = True      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, TensorType]] = None      ,**SCREAMING_SNAKE_CASE_  :	str      ,)       ->	BatchEncoding:





				"""simple docstring"""


				if images is None and text is None:
					raise ValueError('''You have to specify either images or text.'''			)

				# Get only text
				if images is None:
					lowerCAmelCase												=  self.tokenizer
					lowerCAmelCase												=  self.tokenizer(
					    text=SCREAMING_SNAKE_CASE_      ,add_special_tokens=SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,stride=SCREAMING_SNAKE_CASE_      ,pad_to_multiple_of=SCREAMING_SNAKE_CASE_      ,return_attention_mask=SCREAMING_SNAKE_CASE_      ,return_overflowing_tokens=SCREAMING_SNAKE_CASE_      ,return_special_tokens_mask=SCREAMING_SNAKE_CASE_      ,return_offsets_mapping=SCREAMING_SNAKE_CASE_      ,return_token_type_ids=SCREAMING_SNAKE_CASE_      ,return_length=SCREAMING_SNAKE_CASE_      ,verbose=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
					return text_encoding

				# add pixel_values
				lowerCAmelCase												=  self.image_processor(SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_			)

				if text is not None:
					lowerCAmelCase												=  self.tokenizer(
					    text=SCREAMING_SNAKE_CASE_      ,add_special_tokens=SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,stride=SCREAMING_SNAKE_CASE_      ,pad_to_multiple_of=SCREAMING_SNAKE_CASE_      ,return_attention_mask=SCREAMING_SNAKE_CASE_      ,return_overflowing_tokens=SCREAMING_SNAKE_CASE_      ,return_special_tokens_mask=SCREAMING_SNAKE_CASE_      ,return_offsets_mapping=SCREAMING_SNAKE_CASE_      ,return_token_type_ids=SCREAMING_SNAKE_CASE_      ,return_length=SCREAMING_SNAKE_CASE_      ,verbose=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
				else:
					lowerCAmelCase												=  None

				if text_encoding is not None:
					encoding_image_processor.update(SCREAMING_SNAKE_CASE_			)

				return encoding_image_processor






			def 					UpperCAmelCase							(self  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	str			)       ->	Union[str, Any]:





				"""simple docstring"""


				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Any:





				"""simple docstring"""


				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	Dict			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  self.tokenizer.model_input_names
				lowerCAmelCase												=  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


